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Featured in Development

Peter Alvaro talks about the reasons one should engage in language design and why many of us would (or should) do something so perverse as to design a language that no one will ever use. He shares some of the extreme and sometimes obnoxious opinions that guided his design process.

Featured in AI, ML & Data Engineering

Today on The InfoQ Podcast, Wes talks with Katharine Jarmul about privacy and fairness in machine learning algorithms. Jarul discusses what’s meant by Ethical Machine Learning and some things to consider when working towards achieving fairness. Jarmul is the co-founder at KIProtect a machine learning security and privacy firm based in Germany and is one of the three keynote speakers at QCon.ai.

Featured in Culture & Methods

Organizations struggle to scale their agility. While every organization is different, common patterns explain the major challenges that most organizations face: organizational design, trying to copy others, “one-size-fits-all” scaling, scaling in siloes, and neglecting engineering practices. This article explains why, what to do about it, and how the three leading scaling frameworks compare.

Katharine Jarmul and Ethical Machine Learning

Today on The InfoQ Podcast, Wes talks with Katharine Jarmul about privacy and fairness in machine learning algorithms. Katharine discusses what’s meant by Ethical Machine Learning and some things to consider when working towards achieving fairness. Katharine is the Co-Founder at KIProtect a machine learning security and privacy firm based in Germany and is one of the three keynotes at QCon.ai.

Key Takeaways

Ethical machine learning is about practices and strategies for creating more ethical machine learning models. There are many highly publicized/documented examples of machine learning gone awry that show the importance of the need to address ethical machine learning.

Some of the first steps to prevent bias in machine learning is awareness. You should take time to identify your team goals and establish fairness criteria that should be revisited over time. This fairness criteria then can be used to establish the minimum fairness criteria allowed in production.

Laws like GDPR in the EU and HIPAA in the US provide privacy and security to users and have legal implications if not followed.

Adversarial examples (like the DolphinAttack that used subsonic sounds to activate voice assistants) can be used to fool a machine learning model into hearing or seeing something that’s not there. More and more machine learning models are becoming an attack vector for bad actors.

Machine learning is always an iterative process.

Zero-Knowledge Computing (or Federated Learning) is an example of machine learning at the edge and is designed to respect the privacy of an individual’s information.

Sponsored by Gremlin

Today's episode is sponsored by Gremlin. Gremlin's Chaos Engineering platform lets you intentionally inject failture into your system to find weaknesses before they cause problems for your users. Use Gremlin for Free at gremlin.com/free.

Show notes will follow shortly.

About QCon

QCon is a practitioner-driven conference designed for technical team leads, architects, and project managers who influence software innovation in their teams. QCon takes place 8 times per year in London, New York, San Francisco, Sao Paolo, Beijing, Guangzhou & Shanghai. QCon New York is at its 8th Edition and will take place Jun 24-26, 2019. 140+ expert practitioner speakers, 1000+ attendees and 18 tracks will cover topics driving the evolution of software development today. Visit qconnewyork.com to get more details.

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